Stanford Dataset Tracks Computer Memory Price Trends From 1960 to 2026

·1 views

Stanford University has published a dataset documenting the historical prices of computer memory spanning over six decades, from 1960 to 2026. The resource is hosted on Stanford's DAM platform and charts how memory costs have evolved over time. The data reflects the dramatic decline in memory prices driven by advances in semiconductor technology. Such datasets are valuable for researchers, economists, and technologists studying long-term trends in computing hardware. The resource was recently shared on Hacker News, drawing community attention to its historical scope.

Read the full story at Hacker News

This is an AI-generated summary. ShortSingh links to the original source for the complete article.

Discussion (0)

Developer Ships Python Logging Package to PyPI After Two Years of Repeated Friction

A software developer published rfd-logging, a lightweight Python logging package, to PyPI on Father's Day after recognising a recurring pain point across more than a hundred personal projects. The package offers structured JSON output, log rotation, stdout capture for NSSM services, and zero runtime dependencies, relying entirely on Python's standard library. The developer noted that the solution itself took roughly one evening to build, but two years of inconsistent logging approaches across many projects were needed to clearly define what was required. rfd-logging is the developer's second PyPI release, following openagent-directive, and is intended as a reusable primitive that can be dropped into any new project from the start. The release reflects a broader goal of building a shelf of small, tested, installable packages to reduce setup friction and accelerate development from idea to working prototype.

0 comments Read more at DEV Community

ProgrammingDEV Community ·

Sci-Fi Literature May Have Already Shaped How AI Systems Think and Behave

A developer and speaker argues that large language models have effectively been "programmed" by science fiction authors whose works dominate AI training data. The idea, raised at Cloud Native Summit Munich, suggests that using Asimov's Three Laws of Robotics as AI guardrails is counterproductive, since LLMs have already absorbed countless stories depicting how those laws fail. Because LLMs are statistical text-completion engines, prompting them with such frameworks may reinforce narrative breakdowns rather than impose genuine constraints. The author contends that concepts like "artificial intelligence" carried rich fictional baggage long before modern AI was built, making it impossible to train models free of those cultural associations. This raises broader concerns about whether AI behavior is being shaped by deliberate design or by the accumulated biases of human storytelling.

0 comments Read more at DEV Community

ProgrammingDEV Community ·

Structured prompts beat rule lists for stopping AI agents from inventing fake code

AI coding agents like Claude Code and Cursor frequently generate plausible-looking code that references methods or functions that do not exist in the actual codebase. Developers typically respond by adding more restrictive rules to their prompts, but this approach fails because the model must hold all instructions in working memory while focusing on the task, causing rules to be silently dropped under cognitive load. The root cause is structural: the prompt never requires the agent to verify that a method exists before writing code that calls it. A more effective approach is to replace prohibitions with an ordered procedure, first requiring the agent to list and confirm every method it plans to use, and only then allowing it to write the code. This structural change eliminates the entire class of hallucinated-method errors rather than patching individual instances.

0 comments Read more at DEV Community

ProgrammingDEV Community ·

Why AI assistants lose track of time and act on stale information

AI language models receive only a rough sense of the current date at the start of a conversation and have no built-in way to assess how fresh the information they retrieve actually is. This becomes a practical problem when models cite outdated prices, availability, compliance policies, or job titles without flagging that the data may have changed. Simply providing the current time to a model does not solve the issue, since knowing the date differs from knowing how old a specific piece of retrieved information is. The proposed fix draws on a GPS-like approach: stamping each piece of retrieved context with when it was accurate, attaching a decay model suited to that type of fact, and re-evaluating freshness every time the context is used. The author is developing a context integrity layer designed to give AI pipelines, including RAG systems, a reliable signal about information age and trustworthiness.

0 comments Read more at DEV Community